What Do You Desire? What Do You Fear? Theorize it! Teaching Political Theory through Utopian Writing
Bibliographic record
Abstract
Undergraduate students should not just study political theory. They should theorize. Writing-intensive political theory courses can help them do so sooner. By preparing an original political vision, a utopia or a dystopia, throughout the course of the semester, students read and compare the texts of the course against their own emerging texts and move into more critical and systematic political analysis. As a political theorist, my focus is not so much on utopias or dystopias as a subject of study per se , but on tapping into the creative freedom, critical distance, and hard-hitting insights of these traditions while teaching writing. I take seriously Berlin's above-stated concern for the “place and mode of operation” conveyed to the student through the process of learning to write. Visionary writing accelerates students' appreciation of the complexity of another's theory, but also of their own standpoint and capacity for agency and judgment. Khristina Haddad is assistant professor, department of political science, Moravian College. She teaches a writing-intensive course on visionary political writing and is affiliated with German Studies and Women's Studies. Her research interests include politics of time and temporality, Hannah Arendt, political action, fear, feminist theory, women's studies, and, in particular, the politics of women's health. I am greatly indebted to friends and colleagues who helped me along at various stages including (in alphabetical order) Robert Humanick, Eleanor Linn, Bob Mayer, Karla Morales, Laurie Naranch, Gary Olson, Miguelina Ortiz, Martha Reid, Joanna Vecchiarelli Scott, Lyman Tower Sargent, Joel Wingard, and Elizabeth Wingrove. Thanks go also to all those whose dedicated work inspires student writers and teachers of writing at the Gayle Morris Sweetland Writing Center at the University of Michigan, to helpful commentators at the Society for Utopian Studies' Annual Meeting in Toronto, to two anonymous reviewers at PS , and to three groups of students who shared their visions with me.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".